Even relatively simple microorganisms, which have been extensively studied, are hosts to a complex network of interconnected processes occurring on diverse time scales. The multilevel nature of the regulatory network of cells and the interactions occurring at the intra cellular level further augment this complexity (Yokobayashi et al., 2003). Attempts to wholly model the function of even a single cell are currently non trivial, if not impossible, as the amount of delicate intracellular measurements required to validate such a model is exhaustive both in terms of labour and cost. Uncertainties introduced both on the parameter identifiability and on the mechanistic level further complicate this task. The large number of biological data generated with the advancement of a variety of high-throughput experimental technologies demand for the development of comprehensive mathematical model building methods able to capture the complex phenomena occurring within a cell (Covert et al., 2001). Borrowing the fundamental research principles from the Process Systems Engineering paradigm, mathematical modelling of biological systems can provide a systematic means to quantitatively study the characteristics of the multilevel interactions that occur in cell culture. In the present thesis, an integrated modelling framework is established that can ensure the seamless interaction of experimental biology with the development of quantitative mathematical descriptions of biological systems. The use of model-based techniques can facilitate the reduction of unnecessary experimentation and reduce labour and operating costs by identifying the most informative experiments and providing strategies to optimise and automate the bio-process at hand. Paving the way towards a ‘closed-loop’ approach for bio-process automation (Kiparissides et al., 2011), the work herein presents a biological model development framework following a step by step approach, highlighting challenges and “real life” problems associated with each stage of model development. By organising available information in a systematic way, unnecessary experimentation is avoided and models with a priori objectives can be established to guide the in vivo process through the in silico representation. The proposed methodology combines macroscopic and subcellular model development, parameter estimation, global sensitivity analysis, model based design of experiments and selection of optimal feeding policies via dynamic optimisation methods in a fromalised structure. The combined mathematical and experimental framework for the control and optimisation of mammalian cell culture systems, presented herein, is experimentally validated via the succesfull model based optimisation of antibody secreting GS-NS0 cell cultures.